Layer 4 · Inference serving engine

vLLM

An open-source engine for serving large language models, with memory handling built for high concurrent throughput.

Desk Research · September 2026

Evaluated from public documentation, architecture material and source code. No vendor contact.

What it solves

Serves substantially more concurrent requests per accelerator than a default serving setup. Since the hardware is billed by the hour either way, throughput per accelerator is what decides your inference bill.

Who it suits

Any team running an open-weight model in production, and anyone whose inference costs are growing faster than their usage.

What to watch out for

It is a serving engine, not a platform. Fleet management, request routing, multi-tenancy and observability are separate decisions that sit above it.

Every product here gets one of these. A recommendation without a trade-off is not a recommendation.

Problems this comes up for

Our inference costs rise faster than our usage It works in the pilot but not in production We are locked into one model provider

Worth comparing against

LiteLLM

L4 · Inference gateway

Desk Research

A gateway presenting one consistent interface across many hosted model providers and your own self-hosted models.

Fit: Applications calling more than one model provider, or planning to.
Catch: Adds a network hop, and sits on the critical path of every request.

Read the notes

Wondering whether vLLM is the right choice?

Describe your setup and constraints. We will tell you whether it fits, and what else you should be looking at.

Request an assessment hello@nugenit.in